1. Employing Cognitive Calculus for Determinations of Relevance in NLU Daniel Arista, Matthew Peveler, Rikhiya Ghosh, Selmer Bringsjord, & Paul Bello {aristd, pevelm, ghoshr3, selmer}@rpi.edu, paul.bello@nrl.navy.mil

2. Presentation Outline Part I : A Use Case & the NLU Challenge it Presents Part II : One Theory of Conditional Reasoning Part III : The Cognitive Calculus Approach Part IV : Implementation Details Part V : Technology Demonstration

3. Part I A Use Case & the NLU Challenge it Presents

4. Use Case MedBot’s mission is to “identify and transport casualties”

5. Commander1 at t0: “If you need a MedEvac, it’s at location ECHO!" MedBot at t1: “Copy." Commander2 at t2: “MedBot, do you know where the MedEvac is?“ How should MedBot answer at t3? Where would he transport a casualty to? Human/Robot Interaction Use Case

6. “If you need it, the MedEvac is at location ECHO!" Interpret as a conditional or a ‘relevant’ fact?

7. Part II One Theory of Conditional Reasoning

8. The Mental Model Theory of Deductive Reasoning Phillip Johnson-Laird (1975)

9. Mental Models of Conditionals Johnson-Laird, Byrne (2002)

10. Modulating Meaning of Conditionals The principle of semantic modulation: The meanings of the antecedent and consequent, and coreferential links between these two clauses, can add information to models… The principle of pragmatic modulation: The context of a conditional depends on general knowledge in long-term memory and knowledge of the specific circumstances of its utterance. Johnson-Laird, Byrne (2002)

11. Semantic Relevance Ten Sets of Possibilities for Interpreting Conditionals The relevance interpretation. Content or context precludes the possibility in the conditional interpretation in which the consequent does not occur. Typical examples occur when the antecedent asserts merely a condition to which the consequent may be relevant, for example, “If you are interested in Vertigo, then it is on TV tonight.” Individuals know that the antecedent possibility has no bearing on the occurrence of the consequent, and so the consequent holds in any case… Johnson-Laird, Byrne (2002)

12. The Effect of Modulation … is logically equivalent to c, the consequent

13. MMT Shortcomings …what is relevance?...how does modulation occur?

14. Desiderata Other capabilities one might find in a cognitive agent: meta-cognition, planning, decision-making, etc. Multi-modal reasoning (epistemic, deontic, bouletic, etc.)

15. Part III The Cognitive Calculus Approach

16. Cognitive Calculi CC DCEC* DCSC*CECCSCD y CEC*…… purely extensional level: FOL SOLTOLIFOL … … … … … … dialects: PA theories: ZFC axiomatic physics … inference schemas ATPs: SPASS-T SNARKShadowProvermodel finders:MACE … … analogico-deductive reasoninganalogico-inductive reasoning

17. DCEC*DCSC*CECCSCD y CEC*…… purely extensional level: FOLSOLTOLIFOL … … … … … … dialects: PA theories: ZFC axiomatic physics … inference schemas model finders:MACE … … analogico-deductive reasoninganalogico-inductive reasoning And NLU in Connection with Cognitive Calculi? ATPs: SPASS-TSNARKShadowProver

18. Event Calculus Kowalski & Sergot (1986)

19. Deontic Cognitive Event Calculus*

20. Excerpt from Original Dialect of CEC Arkoudas-Bringsjord (2009)

21. parse to intermediary form mapping to formulae knowledge-base of a background theory string Towards NLU-infused Cognitive Calculi

22. Part IV Implementation Details

23. Some Definitions Information is relevant if it is a necessary part of a plan, or if it invalidates it. A plan is a proof from an agent’s situation to a goal. A situation is the set of an agent’s beliefs, knowledge (including common knowledge), obligations, intentions, and desires at some time.

24. 0. Parsing “Relevance” Conditionals S(a,b,“If you want to watch Game of Thrones (GoT), I recorded it") AddGoal(a*,Watch(a*,GoT)) → Recorded(a,GoT)

25. 0. Parsing “Relevance” Conditionals S(a,b,“If you need a MedEvac, it's at location ECHO!") AddGoal(... (a*,MedEvac)) → Location(MedEvac,ECHO)

26. 0.5 Recovering Elided ‘Planning’ Verb S(a,a*“If you need a MedEvac, it's at location ECHO!") AddGoal(... (a*,MedEvac)) → Location(MedEvac,ECHO) O(a,t,P(a,t,Casualty(a)),happens(action(a*,Transport(a*,a,MedEvac))))

27. 1.0 Adjudicating Relevance Relation 1.1 Generate a plan from the agent’s situational variables and the goal implied/stated in the antecedent 1.2 Search for the fluent (or its negation) asserted in the consequent.

28. 1.Located(MedBot, "x") 2.Located(Casualty, "y") 3.Located(MedEvac, "z") 4. 5.[forall loc, time] Holds(Located(MedBot, loc), time) & Holds(Located(Casualty, loc),time) 6. & Holds(Located(MedEvac, loc),time) -> Holds(Tranpsort(MedBot, Casualty, MedEvac),time) 7. 8.forall([agent,loc1,loc2,time]) Initiates(Move(agent, loc1, loc2), Located(MedBot, loc2), time) 9.forall([agent,loc1,loc2,time]) Terminates(Move(agent, loc1, loc2), Located(MedBot, loc1), time) 10.forall([agent, object, loc1, loc2]) Holds(Grabbed(agent, object),time) -> Initiates(Move(agent, loc1, loc2), Located(object, loc2), time) 11.forall([agent, object, loc1, loc2]) Holds(Grabbed(agent, object),time) -> Terminates(Move(agent, loc1, loc2), Located(object, loc1), time) 12. 13.forall([agent, loc, time]) Holds(Located(Medbot, loc), time) & Holds(Located(Casualty, loc),time) -> happens(Grab(MedBot, Casualty), time) 14.forall([agent,object,time]) Initiates(Grab(agent, object), Grabbed(agent, object), time) 15. 16.forall([loc1, loc2, time]) loc1 != loc2 -> Happens(Move(MedBot, loc1, loc2), time) 17. 18.Goal: exists([time]) Holds(Transport(MedBot, Casualty, MedEvac), time) 19. 20.------- 21.Required moves: 22. 23.Happens(Move(MedBot, "x", "y"), 0) 24.Happens(Grab(MedBot, Casualty), 1) 25.Happens(Move(MedBot, "y", "z"), 2) 26. 27.Holds(Trasnport(MedBot, Casualty, MedEvac), 2) Introducing some fluents domain axioms Generated plan

29. The Relevance Relation If the fluent in the consequent is a term in the plan, (or it’s negation) …it is relevant Is_Relevant(a*,Location(MedEvac,ECHO),[plan])

30. Resulting List of Formulas A successful search leads the hearer to interpret the utterance as meaning that the speaker believes that the consequent holds at the time of utterance, the hearer would find the consequent relevant b/c it is either necessary for the stated (or implied) plan in the antecedent to be valid or b/c it would make it invalid, and the content of the consequent holds. B(b,Location(MedEvac,ECHO) B(b,B(a,Is_Relevant(b,Location(MedEvac,ECHO),[plan])) B(b,Is_Relevant(b,Location(MedEvac,ECHO),[plan]))

31. Commander1 at t0: “If you need it, the MedEvac is at location ECHO!" MedBot at t1: “Copy." Commander2 at t2: “MedBot, do you know where the MedEvac is?“ How should MedBot answer at t3? Where would he transport a casualty to? Human/Robot Interaction Use Case

32. “If you need it, the MedEvac is at location ECHO!" Interpret as a conditional or a ‘relevant’ fact!

33. Part V Technology Demonstration

34. PAGI-World Psychometric Artificial General Intelligence (PAGI) World is a simulation environment. It is platform independent, free to use, open-source, compatable with AI programs written in almost any programming language; agnostic as possible regarding which AI approach is used; and easy to set up and get started with.

36. Transporting a Casualty O(a,t,P(a,t,Casualty(a)),happens(action(a*,Transport(a*,a,MedEvac)))) Demo 1: MedBot believes MedEvac is in two locations a the same time! #fail Demo 2: MedBot knows where to go, and transports the casualty! #success

37. Technology Demonstration > Hyperlink

38. Grazie per tua attenzione!...domande?

39. Deontic Cognitive Event Calculus* Commander1 says to MedBot, at time t, “ … “. S(Commander1,MedBot*,t,”If you need it, the MedEvac is at location ECHO”) An agent perceives at time t, that an agent is a casualty. P(a,t,Is_Casualty(a)) Agents are obligated to transport any casualty to a MedEvac O(a,t,P(a,t,Is_Casualty(a)),happens(action(a*,Transport(a*,a,MedEvac))))